Invited Speakers

 

Assoc. Prof. Lei Chen

Shandong University, China

 

Speech Title: Abnormal Behavior Detection and Action Localization Based on Deep Learning
 

Abstract:: In recent years, the video surveillance systems are widely used in the fields of urban safety, security management, crime-fighting, and healthcare. The research on abnormal behavior and action localization is crucial to maintain safety and improve the quality of life. However, surveillance environments often present severe conditions, such as fluctuating lighting, the presence of shadows, and adverse weather conditions. These background variations introduce noises for human behavior features and degrades the abnormal behavior detection and action localization performance. To address these problems, we propose a new framework called efficient abnormal behavior detection that simultaneously integrates spatio-temporal feature modeling and long-term dependency modeling. And we design a multidimensional path aggregation network for spatio-temporal action location, which aggregates the features of multiple paths and fuses the corresponding hierarchical features to obtain spatio-temporal behavioural features. The experimental results show the effectiveness of our proposed methods and demonstrate superiority over other related methods. The research findings can be used to identify and intervene in potential threats, accidents, and dangerous situations.

Short bio: Lei Chen received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, and the Ph.D. degree in electrical and computer engineering from University of Ottawa, Ontario, Canada. He is currently an Associate Professor with the School of Information Science and Engineering, Shandong University, China. His research interests include image processing and computer vision, visual quality assessment and pattern recognition, machine learning and artificial intelligence. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Shandong Province, China Postdoctoral Science Foundation, etc. He has published more than 40 papers on top international journals and conferences in recent years including IEEE TIP, Signal Process., ICME, etc. He was awarded the Future Plan for Young Scholars of Shandong University. He served for many international conferences including the ICIGP 2021, CSAI2022, MLCCIM2022, and ICIVC 2023 as Program Chair, Technical Chair or Publicity Chair.
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Assoc. Prof. Rui Chen

Tianjin University, China

Speech Title: Old Photo Restoration Using Adaptive Energy Transition of Latent Spaces
 

Abstract:: Recent old photo restoration works have achieved significant improvement using generative adversarial networks. However, the restoration quality under this generative framework is inevitably affected by the encoded properties of latent spaces, which reflect pivotal semantic information in the recovery process. Exploring the regularities in latent spaces is an important issue in restoration task. In this paper, we propose a novel Energy Transition Generative Adversarial Network (ET-GAN) for old photo restoration. Based on the fact that an energy function can capture the regularities in the data effectively, we propose to learn an energy-based prior model in latent spaces to improve the expressive power of GAN model. Moreover, we employ the Markov Chain Monte Carlo method to sample a batch of latent vectors from the prior distribution as the regularization. By linearly mixing with a regularized latent vector, the original latent vector is transformed along the principal semantic direction and a better latent space can be derived to improve the generation capability of ET-GAN to restore damaged photos with the multiple degradations. Extensive experiments demonstrate that our model can achieve the state-of-the-art performance in terms of numerical metrics and visual effects.

Short bio: Rui Chen received the Ph.D. degree in instrument science from Tsinghua University, China, in 2010. He is currently an associate professor in the School of Microelectronics, Tianjin University. He has authored or coauthored one book and over 70 technical articles in refereed journals and proceedings. His current research interests include generative artificial intelligence technology and cognitive theory.
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